隨著智慧製造與數位轉型發展，製造業逐漸重視以資料支援現場管理與排程決策。然而，資源有限之中小型製造企業常面臨現場資訊分散、資料紀錄不一致與進度追溯困難等問題，導致生產狀態不易即時掌握，也難以形成可供後續分析與決策支援之資料基礎。本研究以一家工業潤滑油製造企業為案例，建構雲端生產進度管理系統，並進一步發展排程決策分析流程。本研究首先運用資訊價值流圖辨識現行生產資訊流斷點，接著透過低程式碼／無程式碼平台整合 Power Apps、Dataverse、Power Automate 與 Power BI，建立工單建立、製程起迄回報、配方與原料勾選、品檢判定、異常登錄、再製處理與視覺化儀表板等功能，將原本分散於紙本、通訊群組與人工確認中的生產資訊，轉換為具時間戳記、欄位結構與工單關聯之事件資料。系統導入後，再將所累積之事件資料轉換為標準工時、有效工時與固定工單集，並建立具機台適用性限制之平行機台排程模型，進一步考量人力上限、單機不可重疊加工與序列相依清洗時間等實務限制，比較 FCFS、簡單排程規則與基因演算法之績效，並以田口方法檢視 GA 參數設定之影響。研究結果顯示，系統導入後資訊自動化比率由 0% 提升至 75%，中心化比率由 25% 提升至 100%，即時化比率由 25% 提升至 100%，資訊前置時間由 2 小時縮短為 1 小時。排程結果方面，GA 於 12 組情境下平均改善率為 8.89%，且所有情境皆較 FCFS 取得改善，改善範圍介於 2.35% 至 20.75%。田口參數分析結果顯示，建議參數雖可使平均改善率由 8.89% 小幅提升至 8.93%，但平均運算時間由 65 秒增加至 218 秒，顯示原始參數在解品質與運算成本間具有較佳實務平衡。整體而言，本研究建立一套由資訊流改善、雲端資料基礎建構、工時參數轉換至排程方法比較之整合流程。研究貢獻在於說明低程式碼／無程式碼平台可協助資源受限製造企業建立可查詢、可追溯之生產資料基礎，並進一步支援工時分析與排程決策，可作為資源受限之配方型批次製造企業推動生產管理數位化與排程改善之參考。

With the development of smart manufacturing and digital transformation, manufacturing firms increasingly rely on data to support shop-floor management and scheduling decisions. However, small and medium-sized manufacturing enterprises often face limitations in human resources, information technology capabilities, and system implementation budgets. In such contexts, shop-floor information is often fragmented, inconsistently recorded, and difficult to trace, making it difficult to monitor production status in real time and establish a traceable and analyzable data foundation for subsequent decision support.
This study focuses on an industrial lubricant manufacturer characterized by high product variety, small-batch production, customized formulas, shared equipment, and product-dependent cleaning requirements. To address these problems, this study constructs a cloud-based production progress management system using a Low-Code/No-Code (LCNC) platform. Information Stream Mapping (iSM) is applied to identify information flow disruptions, and Microsoft Power Apps, Dataverse, Power Automate, and Power BI are integrated to support work order creation, process start and completion recording, inspection judgment, abnormality registration, rework handling, and dashboard visualization.
After system implementation, the accumulated event data are transformed into standard processing time, effective processing time, and fixed work order sets for scheduling analysis. An eligibility-constrained parallel-machine scheduling model is developed by considering machine eligibility, labor capacity limits, non-overlapping processing constraints, and sequence-dependent cleaning time. First Come First Served (FCFS), simple dispatching rules, and a Genetic Algorithm (GA) are compared under the same fixed work order sets and constraints. In addition, the Taguchi method is applied to analyze the influence of GA parameters.
The results show that the cloud-based system improves information flow efficiency. The automation ratio increased from 0% to 75%, the centralization ratio increased from 25% to 100%, and the real-time capability ratio increased from 25% to 100%. The information lead time was reduced from two hours to one hour. In the scheduling analysis, the GA achieved an average improvement rate of 8.89% across 12 scenarios compared with FCFS, and all scenarios showed positive improvements ranging from 2.35% to 20.75%. Overall, this study proposes an integrated process from information flow improvement and cloud-based data construction to scheduling method evaluation, which can serve as a practical reference for resource-constrained formula-based batch manufacturers.

經濟部中小及新創企業署. (2025). 2025 年中小企業白皮書. https://www.sme.gov.tw/article-tw-2345-13928
Binzer, B., Elshan, E., Fürstenau, D., & Winkler, T. J. (2024). Establishing a low-code/no-code-enabled citizen development strategy. MIS Quarterly Executive, 23, 253–273.
Buer, S.-V., Strandhagen, J. O., & Chan, F. T. (2018). The link between Industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda. International Journal of Production Research, 56(8), 2924–2940.
Cheng, B.-W., & Chang, C.-L. (2007). A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm. Expert Systems with Applications, 32(2), 415–421.
Cifone, F. D., Hoberg, K., Holweg, M., & Staudacher, A. P. (2021). ‘Lean 4.0’: How can digital technologies support lean practices? International Journal of Production Economics, 241, 108258.
Dennis, D., & Meredith, J. (2000). An empirical analysis of process industry transformation systems. Management Science, 46(8), 1085–1099.
Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.
Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19–31.
Elmasri, R., & Navathe, S. B. (2016). Fundamentals of database systems (7th ed.). Pearson.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
Henning, G. P. (2009). Production scheduling in the process industries: current trends, emerging challenges and opportunities. Computer Aided Chemical Engineering, 27, 23–28.
Hopp, W. J., & Spearman, M. L. (2011). Factory physics. Waveland Press.
Kallrath, J. (2002). Planning and scheduling in the process industry. OR Spectrum, 24(3), 219–250.
Méndez, C. A., Cerdá, J., Grossmann, I. E., Harjunkoski, I., & Fahl, M. (2006). State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering, 30(6-7), 913–946.
Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.). Springer.
Microsoft Corporation. (2025). Data types (Transact-SQL) - SQL Server. Microsoft Learn. https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql
Microsoft Corporation. (2026). Microsoft Power Platform documentation. https://learn.microsoft.com/en-us/power-platform/
Omrani, N., Rejeb, N., Maalaoui, A., Dabić, M., & Kraus, S. (2022). Drivers of digital transformation in SMEs. IEEE Transactions on Engineering Management, 71, 5030–5043.
Pinedo, M. L. (2022). Scheduling: Theory, algorithms, and systems (6th ed.). Springer Cham. https://doi.org/10.1007/978-3-031-05921-6
Roh, P., Kunz, A., & Wegener, K. (2019). Information stream mapping: Mapping, analysing and improving the efficiency of information streams in manufacturing value streams. CIRP Journal of Manufacturing Science and Technology, 25, 1–13.
Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of Industry 4.0 technologies on Lean principles. International Journal of Production Research, 58(6), 1644–1661.
Ruiz, R., & Maroto, C. (2006). A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. European Journal of Operational Research, 169(3), 781–800.
Sufi, F. (2023). Algorithms in low-code-no-code for research applications: A practical review. Algorithms, 16(2), 108.
Sugimori, Y., Kusunoki, K., Cho, F., & Uchikawa, S. (1977). Toyota production system and kanban system materialization of just-in-time and respect-for-human system. International Journal of Production Research, 15(6), 553–564.
Yang, T., & Fujimoto, T. (2026). Jidoka just-in-time cycle–from genba kaizen to the introduction of artificial intelligence. International Journal of Production Research, 1–19.